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When working with state-space models, proper scaling is important for accurate computations. A state-space model is well scaled when the following conditions exist:

The entries of the

*A*,*B*, and*C*matrices are homogeneous in magnitude.The model characteristics are insensitive to small perturbations in

*A*,*B*, and*C*(in comparison to their norms).

Working with poorly scaled models can cause your model a severe loss of accuracy
and puzzling results. An example of a poorly scaled model is a dynamic system with
two states in the state vector that have units of light years and millimeters. Such
disparate units may introduce both very large and very small entries into the
*A* matrix. Over the course of computations, this mix of
small and large entries in the matrix could destroy important characteristics of the
model and lead to incorrect results.

For more information on the harmful affects of a poorly scaled model, see Scaling State-Space Models to Maximize Accuracy.

You can avoid scaling issues altogether by carefully selecting units to reduce the spread between small and large coefficients.

In general, you do not have to perform your own scaling when using the Control System Toolbox™ software. The algorithms automatically scale your model to prevent loss of accuracy. The automated scaling chooses a frequency range to maximize accuracy based on the dominant dynamics of the model.

In most cases, automated scaling provides high accuracy without your intervention.
For some models with dynamics spanning a wide frequency range, however, it is
impossible to achieve good accuracy at *all* frequencies and some
tradeoff of accuracy in different frequency bands is necessary. In such cases, a
warning alerts you of potential inaccuracies. If you receive this warning, evaluate
the tradeoffs and consider manually adjusting the frequency interval where you most
need high accuracy. For information on how to manually scale your model, see Manually Scale Your Model.

**Note**

For models with satisfactory scaling, you can bypass automated scaling in the
Control System Toolbox software. To do so, set the `Scaled`

property of
your state-space model to `1`

(true). For information on how to
set this property, see the `set`

reference page.

If automatic scaling produces a warning, you can use the
`prescale`

command to manually scale your model and adjust the
frequency interval where you most need high accuracy.

The `prescale`

command includes a Scaling Tool, which you can use
to visualize accuracy tradeoffs and to adjust the frequency interval where this
accuracy is maximized.

To scale your model using the Scaling Tool, perform the following steps:

Open Scaling Tool.

Specify frequency axis limits.

Specify frequency band for maximum accuracy.

Save the scaling.

For an example of using the Scaling Tool on a real model, see Scaling State-Space Models to Maximize Accuracy.

For more information about scaling models from the command line, see the `prescale`

reference page.

To open the Scaling Tool for a state-space model named `sys`

,
type

prescale(sys)

The Scaling Tool resembles one shown in the following figure.

The Scaling Tool contains the following plots:

The

**Frequency Response Gain**plot helps you determine the frequency band over which you want to maximize scaling.For SISO systems, this plot shows the gain of your model. For MIMO systems, the plot shows the principle gain (largest singular value) of your model.

The

**Frequency Response Accuracy**plot allows you to view the accuracy tradeoffs for your model when maximizing accuracy in a particular frequency bands.This plot shows the following information:

Relative accuracy of the response of the original unscaled model in red

Relative accuracy of the response of the scaled model in blue

Best achievable accuracy when using independent scaling at each frequency in brown

When you compute some model characteristics, such as the frequency response or the system zeros, the software produces the exact answer for some perturbation of the model you specified. The

*relative accuracy*is a measure of the worst-case relative gap between the frequency response of the original and perturbed models. The perturbation accounts for rounding errors during calculation. Any relative accuracy value greater than`1`

implies poor accuracy.**Tip**If the blue Scaled curve is close to the brown Pointwise Optimal curve in a particular frequency band, you already have the best possible accuracy in that frequency band.

You can change the limits of the plot axis to view a particular frequency band
of interest in the Scaling Tool. To view a particular frequency band, specify
the band in the **Show response in the frequency band**
fields.

This action updates the frequency axis of the Scaling tool to show the specified frequency band.

**Tip**

To return to the default display, select the **Auto**
check box.

To adjust the frequency band where you want maximum accuracy, set a new
frequency band in the **Maximize accuracy in the frequency
band** fields. You can visualize accuracy tradeoffs by trying out
different frequency bands and viewing the resulting relative accuracy across the
frequency band of interest.

**Note**

You can use the **Frequency Response Gain** plot, which
plots the gain of your model, to view the dynamics in your model to help
determine the frequency band to maximize accuracy.

Each time you specify a new frequency band, the **Frequency Response
Accuracy** plot updates with the result of the new scaling. Compare
the Scaled curve (blue) to the Pointwise Optimal curve (brown) to determine
where the new scaling is nearly optimal and where you need more accuracy.

**Tip**

To return to the default scaling, select the **Auto**
check box.

When you find a good scaling for your model, save the scaled model as follows:

Click

**Save Scaling**.This action opens the

**Save to Workspace**dialog box.In the

**Save to Workspace**dialog box, verify that any of the following items you want to save are selected, and specify variable names for these items.Scaled model

Scaling information, including:

Scaling factors

Frequencies used to test accuracy

Relative accuracy at each test frequency

For details about the scaling information, see the

`prescale`

reference page.

Click

**OK**.This action sets the State-Space (@ss) object

`Scaled`

property of your model to true. When you set this property to`True`

, the Control System Toolbox algorithms skip the automated scaling of the model.